Unfolding Slowly Changing Dimensions in Data Warehouses
Managing data over time in a data warehouse can be a challenging task. This issue becomes increasingly apparent when dealing with dimensions that change over time, a phenomenon known as Slowly Changing Dimensions (SCDs). This blog post will guide you through the concept of Slowly Changing Dimensions, their types, and their role in data warehousing.
What are Slowly Changing Dimensions?
In the realm of data warehousing, a dimension is a structure that categorizes data to enable users to answer business questions. Common dimensions include time, geography, and product.
Slowly Changing Dimensions are dimensions that change over time. The term 'slowly changing' refers to the rate at which the data changes, which is typically slow compared to the transactional data but can vary based on business requirements. Managing these changes is crucial to ensure that historical data is accurate and to enable trend analysis.
Types of Slowly Changing Dimensions
Type 1: Overwriting
Type 1 SCDs overwrite the old data with new data. This approach is commonly used to correct errors in data or when it is not important to maintain a historical record of the data.
For example, let's consider a customer's address in a Customer Dimension. If the customer relocates and their address changes, a Type 1 SCD would simply update the existing address record with the new one. We won't be able to see the previous address or know when the change occurred, but we will always have the most current address.
Type 2: Creating Additional Records
Type 2 SCDs track historical data by creating new records in the dimension table. A unique identifier distinguishes each record, and each one has a start and end date to represent the validity period of that specific record.
For instance, imagine we're tracking a product's price in a Product Dimension. If the price of the product changes, a Type 2 SCD approach would not overwrite the existing record but create a new one with the new price. The old record's end date would be updated to indicate when the price changed, and the new record's start date would correspond to this change date. In this way, we can see the entire price history of the product.
Type 3: Adding New Columns
Type 3 SCDs handle changes by adding new columns to the table. They typically preserve a limited history of the data changes.
Taking the example of an Employee Dimension where we track an employee's position in a company. If an employee gets a promotion, with a Type 3 SCD approach, we wouldn't create a new record nor overwrite the existing one. Instead, we might have two columns: 'Original Position' and 'Current Position'. When the employee gets promoted, we update the 'Current Position' column while the 'Original Position' maintains the first job title. This way, we can see what the original and current positions of an employee are but won't have the full history if the employee held several positions.
Type 4: Using History Tables
In Type 4 SCDs, also known as the 'History Table' approach, a separate table is maintained to store historical data, while the main dimension table only contains current data.
For example, let's consider a Product Dimension where we are tracking the product's price. In a Type 4 SCD, the Product Dimension table would always show the current price. Any changes to the price are stored in a separate 'Product Price History' table, which maintains a record of the old prices along with their validity periods.
Type 5: Combination of Type 1 and Type 4
A Type 5 SCD is essentially a combination of Type 1 and Type 4 approaches. In this scenario, a dimension table is used to store current data (as in Type 1), and a separate history table is used to keep a record of changes (as in Type 4). Additionally, a 'mini-dimension' is added which contains attributes that change rapidly and is associated with the main dimension table.
For instance, consider an Employee Dimension with an associated 'Employee Salary History' table and 'Job Role' mini-dimension. The main table holds current information, the history table holds the salary changes, and the rapidly changing 'Job Role' is maintained separately to avoid excessive changes to the main dimension table.
Type 6: Combination of Type 1, 2, and 3
Type 6 SCD, also known as the 'Hybrid' method, is a combination of Type 1, 2, and 3 approaches. It keeps the latest change as an additional column (like Type 3), creates new records for changes (like Type 2), and also overwrites some attributes (like Type 1).
Suppose we are tracking customer data in a Customer Dimension. In a Type 6 SCD, when a customer changes their address, we would create a new record with the new address (like Type 2), the old record would be updated to indicate it's no longer the current address (like Type 1), and we would have an additional 'Previous Address' column in the new record to indicate the most recent address change (like Type 3).
The Importance of Slowly Changing Dimensions
Understanding and managing SCDs is fundamental to maintaining an effective and accurate data warehouse. Without the proper handling of SCDs, analyses derived from the data warehouse can be misleading or incorrect. Different types of SCDs offer different benefits and are suitable for different situations, and the choice between them will depend on the specific requirements of your data warehouse and the business questions you need to answer.
Conclusion
Slowly Changing Dimensions are a crucial component in the field of data warehousing. They provide a mechanism to manage and track changes in your data over time, thus ensuring that your data warehouse reflects the true state of your business.
Choosing the right type of SCD can be a pivotal decision, and it is crucial to understand the implications of each type on your data warehouse design and on the ability to answer business questions accurately. In this blog, we've discussed the basics of Slowly Changing Dimensions, but remember that each organization may have unique needs that may require further consideration or custom solutions.